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Top Remote Senior Level Data Engineer Jobs in Austin, TX

7 Days Ago
United States
Remote
7,500 Employees
130K-210K Annually
Senior level
7,500 Employees
130K-210K Annually
Senior level
Cloud • Information Technology • Sales • Security • Cybersecurity
The Sr. Machine Learning Engineer at CrowdStrike will innovate machine learning technologies, focusing on scalability and cost-effectiveness, build backend microservices, maintain data pipelines, and contribute to the training of custom models while collaborating across teams and mentoring other engineers.
Top Benefits:
401-K
401-K Matching
Adoption Assistance
+44 More
14 Days Ago
Austin, TX, USA
Remote
Hybrid
4,500 Employees
Senior level
4,500 Employees
Senior level
Cloud • Digital Media • Machine Learning • Mobile • Software • Design
In this role, you will develop AI/ML strategies to enhance customer support at Canva, lead the ML development of natural language processing systems, and collaborate across teams to leverage existing ML capabilities and address cross-organization challenges.
Top Benefits:
401-K
401-K Matching
Company Equity
+56 More
20 Days Ago
Austin, TX, USA
Remote
Hybrid
800 Employees
150K-150K Annually
Senior level
800 Employees
150K-150K Annually
Senior level
eCommerce • Legal Tech • Professional Services • Software • Data Privacy
As a Senior Data Engineer, you will focus on building and maintaining data infrastructure, creating and optimizing ETL pipelines, and collaborating with analysts to ensure efficient data delivery. This role requires innovations in database design and proactive monitoring of data consistency.
Top Benefits:
401-K
401-K Matching
Company Outings
+30 More

Featured Jobs

21 Days Ago
USA
Remote
184 Employees
140K-150K Annually
Senior level
184 Employees
140K-150K Annually
Senior level
Insurance
The Marketing Technology Data Engineer will oversee data architecture for data integration, transformation, and activation. Key responsibilities include building solutions for real-time and batch data management, developing CI/CD data models, improving data quality, and leveraging Google Cloud tools. Collaboration with cross-functional teams is essential to meet product and client needs.
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